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Design and Analysis of Energy-Efficient Dynamic Range Approximate Logarithmic Multipliers for Machine Learning

  • Published:2021-10-01 Issue:4 Volume:6 Page:612-625
  • ISSN:2377-3782
  • Container-title:IEEE Transactions on Sustainable Computing
  • language:
  • Short-container-title:IEEE Trans. Sustain. Comput.

Author:

Yin PeipeiORCID,Wang Chenghua,Waris HaroonORCID,Liu WeiqiangORCID,Han YinheORCID,Lombardi FabrizioORCID

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Six Talent Peaks Project in Jiangsu Province

National Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Control and Optimization,Hardware and Architecture,Renewable Energy, Sustainability and the Environment,Software

Cited by 37 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Energy-Efficient Neural Network Acceleration Using Most Significant Bit-Guided Approximate Multiplier;Electronics;2024-08-01

2. Two energy efficient unsigned approximate multipliers with N-4 compressors;IEICE Electronics Express;2024-07-25

3. Most Significant One-Driven Shifting Dynamic Efficient Multipliers for Large Language Models;2024 IEEE International Symposium on Circuits and Systems (ISCAS);2024-05-19

4. A Configurable Approximate Multiplier for CNNs Using Partial Product Speculation;2024 Design, Automation & Test in Europe Conference & Exhibition (DATE);2024-03-25

5. Approximation- and Quantization-Aware Training for Graph Neural Networks;IEEE Transactions on Computers;2024-02
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